Similar to the method in asymmetric tri-training [1]}, to avoid significant error propagation, we select predictions of high confidence as pseudo labels. Since our model generates probabilities for every predicted answer start and end index, a threshold \({T_{prob}}\)  will be employed to filter low-confidence samples.
